【作 者】：网站采编
【关键词】：
【摘 要】：SHARIF, M. I., LI, J. P., KHAN, M. A. SALEEM, M. A. 2020. Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recognition Letters, 129, 181-189. 图6 深度学习时代下的

SHARIF, M. I., LI, J. P., KHAN, M. A. & SALEEM, M. A. 2020. Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images. Pattern Recognition Letters, 129, 181-189.

图6 深度学习时代下的磁共振成像领域共被引聚类分析（2019-2020）

AVENDI, M. R., KHERADVAR, A. & JAFARKHANI, H. 2016. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Medical Image Analysis, 30, 108-119.

图11 帝国理工学院产出论文的主要合作机构

NAIR, T., PRECUP, D., ARNOLD, D. L. & ARBEL, T. 2020. Exploring uncertainty measures in deep networks for Multiple sclerosis lesion detection and segmentation. Medical Image Analysis, 59, 10.

2013-2015年文章数量还处于零星增长阶段，2016-2017年发文量增长明显，但仍相对为低位，而从2018年起的近五年里则呈现了爆发增长的趋势，并且增长率越来越高。

BERNARD, O., LALANDE, A., ZOTTI, C., et al. 2018. Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? Ieee Transactions on Medical Imaging, 37, 2514-2525.

DENG, Y., REN, Z. Q., KONG, Y. Y., BAO, F. & DAI, Q. H. 2017. A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification. Ieee Transactions on Fuzzy Systems, 25, 1006-1012.

美国埃默里大学的Yang Xiaofeng副教授，Liu Tian副教授、Wang Tonghe助理教授、Lei Yang老师等都较大程度地贡献了该领域的论文产出。

表1 深度学习时代下的磁共振成像领域主要的SCI论文产出机构及影响力

AHMADI, M., SHARIFI, A., FARD, M. J. & SOLEIMANI, N. Detection of brain lesion location in MRI images using convolutional neural network and robust PCA. International Journal of Neuroscience, 12.

Cluster 1 #0 prostate cancer前列腺癌

为了解该领域近年的研究中主要的主题分布情况，该报告以上文中提及的数据源为基础，通过图谱构建工具VOSviewer提取了论文的关键词，并设置了出现频次在10以上的关键词集合通过Co-occurrence共现网络计算获得了图2。

磁共振成像（Magnetic Resonance Imaging, MRI）是现今临床医学诊断的重要手段之一，但如何提高成像技术、应用场景等成为该领域近来的热门研究问题。

SAJJAD, M., KHAN, S., MUHAMMAD, K., et al. 2019. Multi-grade brain tumor classification using deep CNN with extensive data augmentation. Journal of Computational Science, 30, 174-182.

Cluster 6 #5 unified multi-channel classification统一多通道分类

HAMMERNIK, K., KLATZER, T., KOBLER, E., et al. 2018. Learning a variational network for reconstruction of accelerated MRI data. Magnetic Resonance in Medicine, 79, 3055-3071.

AGGARWAL, H. K., MANI, M. P. & JACOB, M. 2019. MoDL: Model-Based Deep Learning Architecture for Inverse Problems. Ieee Transactions on Medical Imaging, 38, 394-405.

QUAN, T. M., NGUYEN-DUC, T. & JEONG, W. K. 2018. Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss. Ieee Transactions on Medical Imaging, 37, 1488-1497.

图13 复旦大学产出论文的主要合作机构

YANG, G., YU, S. M., DONG, H., SLABAUGH, G., et al. 2018. DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction. Ieee Transactions on Medical Imaging, 37, 1310-1321.

LEYNES, A. P., YANG, J., WIESINGER, F., et al. 2018. Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI. Journal of Nuclear Medicine, 59, 852-858.

QIN, C., SCHLEMPER, J., CABALLERO, J., et al. 2019. Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction. Ieee Transactions on Medical Imaging, 38, 280-290.

图5 深度学习时代下的磁共振成像领域共被引聚类分析（2017-2018）

COLE, J. H., POUDEL, R. P. K., TSAGKRASOULIS, D., et al. 2017. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage, 163, 115-124.

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